{"id":50797,"date":"2022-05-12T00:00:00","date_gmt":"2022-05-12T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/telecom-churn-prediction-using-machine-learning-python-and-griddb\/"},"modified":"2026-03-30T14:40:23","modified_gmt":"2026-03-30T21:40:23","slug":"telecom-churn-prediction-using-machine-learning-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/telecom-churn-prediction-using-machine-learning-python-and-griddb\/","title":{"rendered":"\u6a5f\u68b0\u5b66\u7fd2\u3001Python\u3001GridDB\u3092\u7528\u3044\u3066\u901a\u4fe1\u4e8b\u696d\u8005\u306e\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b"},"content":{"rendered":"<p>\u9867\u5ba2\u89e3\u7d04\u7387\u3068\u306f\u3001\u7279\u5b9a\u306e\u4f01\u696d\u3068\u306e\u53d6\u5f15\u3092\u505c\u6b62\u3059\u308b\u9867\u5ba2\u306e\u6570\u3092\u6c7a\u5b9a\u3059\u308b\u91cd\u8981\u306a\u30d3\u30b8\u30cd\u30b9\u6982\u5ff5\u3067\u3059\u3002\u89e3\u7d04\u7387\u306f\u3001\u4f01\u696d\u304c\u4e00\u5b9a\u306e\u671f\u9593\u306b\u9867\u5ba2\u3092\u5931\u3046\u5272\u5408\u3068\u5b9a\u7fa9\u3055\u308c\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u89e3\u7d04\u7387\u304c\u5e7415%\u3067\u3042\u308c\u3070\u3001\u305d\u306e\u4f01\u696d\u306f\u6bce\u5e74\u7dcf\u9867\u5ba2\u6570\u306e15%\u3092\u5931\u3063\u3066\u3044\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u901a\u4fe1\u696d\u754c\u3067\u306f\u3001\u7af6\u4e89\u306e\u6fc0\u5316\u3084\u65b0\u898f\u901a\u4fe1\u4e8b\u696d\u8005\u306e\u51fa\u73fe\u306b\u3088\u308a\u3001\u9867\u5ba2\u96e2\u308c\u304c\u7279\u306b\u91cd\u8981\u8996\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u305f\u3081\u3001\u901a\u4fe1\u696d\u754c\u3067\u306f\u6bce\u5e74\u9ad8\u3044\u89e3\u7d04\u7387\u304c\u4e88\u60f3\u3055\u308c\u307e\u3059\u3002<\/p>\n<blockquote><p>\n  \u901a\u4fe1\u696d\u754c\u306e\u89e3\u7d04\u7387\u306f\u6bce\u6708\u7d041.9\uff05\u3067\u3042\u308a\u3001\u6bce\u5e7467\uff05\u307e\u3067\u4e0a\u6607\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\uff08\u51fa\u5178\uff09\n<\/p><\/blockquote>\n<p>\u3053\u308c\u306f\u9867\u5ba2\u7dad\u6301\u7387\u306b\u76f4\u63a5\u5f71\u97ff\u3059\u308b\u305f\u3081\u3001\u4f01\u696d\u306b\u3068\u3063\u3066\u975e\u5e38\u306b\u614e\u91cd\u306b\u691c\u8a0e\u3059\u3079\u304d\u30ea\u30b9\u30af\u3067\u3059\u3002<\/p>\n<blockquote><p>\n  \u540c\u8a18\u4e8b\u306b\u3088\u308b\u3068\u3001\u901a\u4fe1\u696d\u754c\u3067\u306f\u65b0\u898f\u9867\u5ba2\u7372\u5f97\u30b3\u30b9\u30c8\u306f\u9867\u5ba2\u7dad\u6301\u30b3\u30b9\u30c8\u306e25\u500d\u3067\u3042\u308a\u3001\u3053\u306e\u3053\u3068\u3082\u89e3\u7d04\u7387\u3092\u6c7a\u5b9a\u7684\u306b\u3059\u308b\u8981\u56e0\u306e\u4e00\u3064\u3067\u3059\u3002\n<\/p><\/blockquote>\n<p>\u9ad8\u5ea6\u306a\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u3001\u7d99\u7d9a\u7387\u306e\u3088\u3046\u306a\u30d3\u30b8\u30cd\u30b9\u30b3\u30f3\u30bb\u30d7\u30c8\u3068\u9023\u643a\u3057\u3001\u30d3\u30b8\u30cd\u30b9\u30a4\u30f3\u30c6\u30ea\u30b8\u30a7\u30f3\u30b9\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u5e83\u7bc4\u304b\u3064\u8a73\u7d30\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u3001\u901a\u4fe1\u696d\u754c\u306b\u304a\u3051\u308b\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\u3053\u306e\u76ee\u7684\u306e\u305f\u3081\u306b\u3001Python\u3001GridDB\u3001\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306a\u3069\u306e\u4e00\u9023\u306e\u6280\u8853\u3092\u7d44\u307f\u5408\u308f\u305b\u3001\u5b9f\u969b\u306e\u751f\u7523\u74b0\u5883\u306b\u3053\u306e\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u5c55\u958b\u3057\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u307e\u305a\u5b9f\u884c\u74b0\u5883\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3092\u884c\u3044\u307e\u3059\u3002\u6b21\u306b\u3001\u672c\u7814\u7a76\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080\u305f\u3081\u306b\u5fc5\u8981\u306aPython\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63a2\u7d22\u3059\u308b\u305f\u3081\u306b\u3001\u69d8\u3005\u306aPython\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u5229\u7528\u3057\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001\u4e88\u6e2c\u7d50\u679c\u3092\u5f97\u308b\u305f\u3081\u306b\u8a55\u4fa1\u3059\u308b\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<h2>\u74b0\u5883\u306e\u8a2d\u5b9a<\/h2>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u884c\u3063\u305f\u64cd\u4f5c\u3092\u6210\u529f\u3055\u305b\u308b\u305f\u3081\u306b\u3001\u79c1\u305f\u3061\u306e\u5b9f\u884c\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u518d\u73fe\u3059\u308b\u305f\u3081\u306e\u30ea\u30b9\u30c8\u3092\u4ee5\u4e0b\u306b\u793a\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li>Windows 10, Anaconda, Jupiter Notebook<\/li>\n<li>Python 3.8 &#8211; <a href=\"https:\/\/github.com\/griddb\/python_client\/releases\/tag\/0.8.3\">MSI for GridDB Python Client 0.8.3<\/a> <\/li>\n<li><a href=\"https:\/\/github.com\/griddb\/c_client\/releases\/tag\/v4.5.0\">MSI for GridDB C Client<\/a><\/li>\n<li>Swig 4.0.2<\/li>\n<\/ul>\n<p>pip\u3092\u7528\u3044\u305fGridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u3064\u3044\u3066\u306f\u3001\u4ee5\u4e0b\u3092\u3054\u53c2\u7167\u304f\u3060\u3055\u3044\u3002<br \/>\n<a href=\"https:\/\/pypi.org\/project\/griddb-python-client\/\">pip install griddb-python-client<\/a><br \/>\n<a href=\"https:\/\/pypi.org\/project\/griddb-python\/\">pip install griddb-python<\/a><\/p>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7d39\u4ecb<\/h2>\n<p>\u4eca\u56de\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30017043\u884c\u3067\u69cb\u6210\u3055\u308c\u308b\u4ee3\u8868\u7684\u306a\u3082\u306e\u3067\u3001\u305d\u308c\u305e\u308c\u304c\u9867\u5ba2\u3092\u8868\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f27\u306e\u5c5e\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u3042\u308a\u3001\u4ee5\u4e0b\u306e<a href=\"https:\/\/www.kaggle.com\/bandiatindra\/telecom-churn-prediction\/notebook\">Kaggle notebook<\/a>\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u8a18\u4e8b\u306e\u5f8c\u534a\u3067\u8a00\u53ca\u3055\u308c\u308b\u91cd\u8981\u306a\u5c5e\u6027\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li><code>gender<\/code>: \u304a\u5ba2\u69d8\u304c\u7537\u6027\u304b\u5973\u6027\u304b<\/li>\n<li><code>SeniorCitizen<\/code>: \u304a\u5ba2\u69d8\u304c\u9ad8\u9f62\u8005\u304b\u3069\u3046\u304b<\/li>\n<li><code>tenure<\/code>: \u304a\u5ba2\u69d8\u3068\u306e\u53d6\u5f15\u6708\u6570<\/li>\n<li><code>OnlineSecurity<\/code>: \u304a\u5ba2\u69d8\u304c\u30aa\u30f3\u30e9\u30a4\u30f3\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u3092\u304a\u6301\u3061\u304b\u3069\u3046\u304b<\/li>\n<li>\u305d\u306e\u4ed6 <code>PhoneService<\/code>, <code>MultipleLines<\/code>, <code>InternetService<\/code> \u306a\u3069\u306e\u5c5e\u6027\u304c\u3042\u308a\u307e\u3059<\/li>\n<\/ul>\n<p>\u5c5e\u6027\u3084\u305d\u306e\u7a2e\u985e\u306b\u3064\u3044\u3066\u306f\u3001\u4ee5\u964d\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u3055\u3089\u306b\u691c\u8a0e\u3057\u307e\u3059\u3002<\/p>\n<h2>\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n<p>\u672c\u8a18\u4e8b\u3067\u8aac\u660e\u3059\u308b\u51e6\u7406\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306b\u3001\u3044\u304f\u3064\u304b\u306ePython\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002Jupyter\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u3001\u4ee5\u4e0b\u306e\u884c\u3092\u633f\u5165\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">\nimport numpy as nump # linear algebra\nimport pandas as pand #  data processing, read CSV file\nimport seaborn as seab#  data visualization\nimport matplotlib.pyplot as plot #calculate plots\nimport griddb_python as griddb #application database\n<\/code><\/pre>\n<\/div>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080\u305f\u3081\u306b\u3001\u524d\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u305fpandas\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>telecom_customers = pand.read_csv('Churn.csv')<\/code><\/pre>\n<\/div>\n<p>pandas\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u62bd\u51fa\u3057\u307e\u3059\u3002head\u95a2\u6570\u306e\u304a\u304b\u3052\u3067\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>telecom_customers.head()<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">customerID   gender  SeniorCitizen   Partner Dependents  tenure  PhoneService    MultipleLines   InternetService OnlineSecurity  ... DeviceProtection    TechSupport StreamingTV StreamingMovies Contract    PaperlessBilling    PaymentMethod   MonthlyCharges  TotalCharges    Churn\n0   7590-VHVEG  Female  0   Yes No  1   No  No phone service    DSL No  ... No  No  No  No  Month-to-month  Yes Electronic check    29.85   29.85   No\n1   5575-GNVDE  Male    0   No  No  34  Yes No  DSL Yes ... Yes No  No  No  One year    No  Mailed check    56.95   1889.5  No\n2   3668-QPYBK  Male    0   No  No  2   Yes No  DSL Yes <\/code><\/pre>\n<\/div>\n<p>GridDB \u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u5168\u5c5e\u6027\u3092\u53d6\u5f97\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u4ee5\u4e0b\u306e\u884c\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>telecom_customers.columns.values<\/code><\/pre>\n<\/div>\n<p>\u3053\u306e\u30b3\u30de\u30f3\u30c9\u306f\u3001\u3059\u3079\u3066\u306e\u5c5e\u6027\u540d\u3092\u542b\u3080\u914d\u5217\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',\n       'TotalCharges', 'Churn'], dtype=object)\n<\/code><\/pre>\n<\/div>\n<p>\u305f\u3060\u3057\u3001GridDB \u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u3068\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u3053\u308c\u3089\u306e\u5c5e\u6027\u306e \u30c7\u30fc\u30bf\u578b\u3092\u77e5\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u305f\u3081\u306b\u3001\u4ee5\u4e0b\u306e\u884c\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>Telecom_customers.dtypes<\/code><\/pre>\n<\/div>\n<p>\u305d\u3057\u3066\u3001\u3059\u3079\u3066\u306e\u5c5e\u6027\u306e\u578b\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">customerID           object\ngender               object\nSeniorCitizen         int64\nPartner              object\nDependents           object\ntenure                int64\nPhoneService         object\nMultipleLines        object\nInternetService      object\nOnlineSecurity       object\nOnlineBackup         object\nDeviceProtection     object\nTechSupport          object\nStreamingTV          object\nStreamingMovies      object\nContract             object\nPaperlessBilling     object\nPaymentMethod        object\nMonthlyCharges      float64\nTotalCharges         object\nChurn                object\ndtype: objec<\/code><\/pre>\n<\/div>\n<p>\u307e\u305a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e NULL \u5024\u3092\u691c\u51fa\u3057\u3066\u3001\u4efb\u610f\u306e\u5024\u306b\u7f6e\u304d\u63db\u3048\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u307e\u3059\u3002\u307e\u305f\u3001customerID \u30ab\u30e9\u30e0\u306f\u3001\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u30e2\u30c7\u30eb\u306b\u306f\u95a2\u4fc2\u306a\u3044\u306e\u3067\u524a\u9664\u3057\u307e\u3059\u3002\u3055\u3089\u306b\u3001GridDB \u306e\u30c7\u30fc\u30bf\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u306b\u5408\u308f\u305b\u3066\u3001churn \u5c5e\u6027\u306e\u5024\u3092 Yes\/No \u304b\u3089 True\/False \u306e\u771f\u507d\u5024\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059\u3002\u5b9f\u969b\u3001\u307b\u3068\u3093\u3069\u306e\u5c5e\u6027\u3092\u771f\u507d\u5024\u3067\u5b9a\u7fa9\u3059\u308b\u306e\u3067\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u3059\u3079\u3066\u306e\u30ab\u30c6\u30b4\u30ea\u5c5e\u6027\u3067\u3053\u306e\u51e6\u7406\u3092\u884c\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">\ntelecom_customers.dropna(inplace = True)\n \ndataframe = telecom_customers.iloc[:,1:]\n\ndataframe['Churn'].replace(to_replace='Yes', value=True, inplace=True)\ndataframe['Churn'].replace(to_replace='No',  value=False, inplace=True)\n<\/code><\/pre>\n<\/div>\n<p>\u307e\u305f\u3001\u30ab\u30c6\u30b4\u30ea\u5c5e\u6027\u306f\u3059\u3079\u3066\u30c0\u30df\u30fc\u5909\u6570\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059\u3002\u3053\u306e\u30c0\u30df\u30fc\u5909\u6570\u304c\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u69cb\u7bc9\u306b\u4f7f\u7528\u3055\u308c\u308b\u3053\u3068\u306f\u3001\u5f8c\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataframeDummies = pand.get_dummies(dataframe)\ndataframeDummies.head()\n<\/code><\/pre>\n<\/div>\n<p>\u6027\u5225\u306e\u3088\u3046\u306a\u5c5e\u6027\u306f\u3001\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3068\u3057\u3066\u3067\u306f\u306a\u304f\u3001\u771f\u507d\u5024\u3068\u3057\u3066\u6271\u308f\u308c\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u3092\u8e0f\u307e\u3048\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5404\u5c5e\u6027\u3092GridDB\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u3053\u3068\u3067\u3001GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30e2\u30fc\u30c9\u306e\u69cb\u7bc9\u3092\u958b\u59cb\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">conInfo = griddb.ContainerInfo(\"column1\",\n                    [[\"customerID\", griddb.Type.STRING],\n                    [\"gender\", griddb.Type.STRING],\n                    [\"SeniorCitizen\", griddb.Type.BOOL],\n                    [\"Partner\", griddb.Type.BOOL],\n                    [\"Dependents\", griddb.Type.BOOL],\n                    [\"tenure\", griddb.Type.LONG],\n                    [\"PhoneService\", griddb.Type.BOOL],\n                    [\"MultipleLines\", griddb.Type.BOOL],\n                    [\"InternetService\", griddb.Type.STRING],\n                    [\"OnlineSecurity\", griddb.Type.BOOL],\n                    [\"OnlineBackup\", griddb.Type.BOOL],\n                    [\"DeviceProtection\", griddb.Type.BOOL],\n                    [\"TechSupport\", griddb.Type.BOOL],\n                    [\"StreamingTV\", griddb.Type.BOOL],\n                    [\"StreamingMovies\", griddb.Type.BOOL],\n                    [\"Contract\", griddb.Type.String],\n                    [\"PaperlessBilling\", griddb.Type.BOOL],\n                    [\"PaymentMethod\", griddb.Type.STRING],\n                    [\"MonthlyCharges\", griddb.Type.FLOAT],\n                    [\"TotalCharges\", griddb.Type.FLOAT],\n                    [\"Churn\", griddb.Type.BLOB]],\n                    griddb.ContainerType.COLLECTION, True)\n    col = gridstore.put_container(conInfo)\n<\/code><\/pre>\n<\/div>\n<p>GridDB\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3084\u30b9\u30c8\u30a2\u306e\u53d6\u5f97\u3001\u30b3\u30ec\u30af\u30b7\u30e7\u30f3\u306e\u4f5c\u6210\u65b9\u6cd5\u306b\u3064\u3044\u3066\u306f\u3001GitHub\u306e\u516c\u5f0f\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u8907\u6570\u306e<a href=\"https:\/\/github.com\/griddb\/python_client\/tree\/master\/sample\"> Python\u3067\u306eGridDB\u7528\u30b5\u30f3\u30d7\u30eb<\/a>\u304c\u63b2\u8f09\u3055\u308c\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u305d\u3061\u3089\u3092\u3054\u89a7\u4e0b\u3055\u3044\u3002<\/p>\n<p>\u30e2\u30c7\u30eb\u306e\u4e3b\u30ad\u30fc\u3067\u3042\u308bcustomerID\u306b\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8ffd\u52a0\u3059\u308b\u3053\u3068\u3092\u5fd8\u308c\u306a\u3044\u3067\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\"> col.create_index(\"customerID\", griddb.IndexType.DEFAULT)\n<\/code><\/pre>\n<\/div>\n<p>\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u3001GridDB\u306b\u683c\u7d0d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">filename = 'churn.csv'\n    with open(filename, 'r') as csvfile:\n    datareader = csv.reader(csvfile)\n    for row in datareader:\n        toGriddb = col.put(row)\n    col.commit();\n<\/code><\/pre>\n<\/div>\n<p>\u30c7\u30fc\u30bf\u304c\u6b63\u3057\u304f\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3055\u308c\u305f\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306b\u3001\u30af\u30a8\u30ea\u30fc\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>query=col.query(\"select * where gender = 'Female'\")<\/code><\/pre>\n<\/div>\n<p>GridDB\u304c\u6b63\u5e38\u306b\u30c7\u30fc\u30bf\u3092\u4fdd\u6301\u3059\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f\u5f8c\u3001\u63a2\u7d22\u7684\u306a\u30c7\u30fc\u30bf\u5206\u6790\u3092\u7d9a\u3051\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>\u63a2\u7d22\u7684\u30c7\u30fc\u30bf\u89e3\u6790<\/h2>\n<p>\u3053\u306e\u6642\u70b9\u3067\u3001\u63a2\u7d22\u7684\u306a\u30c7\u30fc\u30bf\u5206\u6790\u3092\u884c\u3046\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002\u307e\u305a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5c5e\u6027\u3068\u3001\u4eca\u56de\u306e\u7814\u7a76\u306e\u4e3b\u773c\u3067\u3042\u308b\u89e3\u7d04\u5c5e\u6027\u3068\u306e\u76f8\u95a2\u3092\u78ba\u7acb\u3059\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u76f8\u95a2\u95a2\u4fc2\u3092\u5f97\u308b\u306b\u306f\u3001\u6b21\u306e\u3088\u3046\u306a\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">plot.figure(figsize=(15,8))\ndataframe_dummies.corr()['Churn'].sort_values(ascending = False).plot(kind='bar')\n<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/churncorrelation.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/churncorrelation.png\" alt=\"\" width=\"882\" height=\"669\" class=\"aligncenter size-full wp-image-28126\" srcset=\"\/wp-content\/uploads\/2022\/03\/churncorrelation.png 882w, \/wp-content\/uploads\/2022\/03\/churncorrelation-300x228.png 300w, \/wp-content\/uploads\/2022\/03\/churncorrelation-768x583.png 768w, \/wp-content\/uploads\/2022\/03\/churncorrelation-600x455.png 600w\" sizes=\"(max-width: 882px) 100vw, 882px\" \/><\/a><\/p>\n<p>\u3053\u306e\u76f8\u95a2\u30b0\u30e9\u30d5\u306e\u7d50\u679c\u3092\u5206\u6790\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u53f3\u304b\u3089\u9806\u306b\u3001\u89e3\u7d04\u306e\u5c5e\u6027\u3092\u89b3\u5bdf\u3059\u308b\u3068\u3001\u4e88\u60f3\u901a\u308a\u3001\u305d\u308c\u81ea\u4f53\u3068\u306e\u76f8\u95a2\u306f1\u3067\u3059\u3002\u307e\u305f\u3001\u89e3\u7d04\u3068\u76f8\u95a2\u304c\u9ad8\u3044\u5c5e\u6027\u306f\u3001\u5951\u7d04 (contracts)\u3001\u30aa\u30f3\u30e9\u30a4\u30f3\u30fb\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3 (online security)\u3001\u30c6\u30af\u30cb\u30ab\u30eb\u30fb\u30b5\u30dd\u30fc\u30c8 (technical support) \u3067\u3059\u3002\u4e00\u65b9\u3001\u30b0\u30e9\u30d5\u306e\u53f3\u5074\u3092\u898b\u3066\u307f\u308b\u3068\u30012\u5e74\u5951\u7d04 (two-year contracts)\u3001\u9867\u5ba2\u3068\u306e\u53d6\u5f15\u6708\u6570 (tenure) \u306f\u3001\u89e3\u7d04\u3068\u8ca0\u306e\u76f8\u95a2\u304c\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n<p>\u3055\u3066\u3001\u7814\u7a76\u306e\u8208\u5473\u306b\u5fdc\u3058\u3066\u3001<code>matplotlib.ticker<\/code> \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u7528\u3044\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u69d8\u3005\u306a\u5909\u6570\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30c7\u30e2\u306e\u76ee\u7684\u306e\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306egender\u5909\u6570\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3001\u7537\u6027\u3068\u5973\u6027\u306e\u9867\u5ba2\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u3092\u898b\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u6b21\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">colors = ['#000080','#FF0000']\nax = (telecom_customers['gender'].value_counts()*100.0 \/len(telecom_customers)).plot(kind='bar',stacked = True,rot = 0,color = colors)\n\nax.set_ylabel('Percent. Customers')\nax.set_xlabel('Gender')\nax.set_ylabel('Percent. Customers')\nax.set_title('Gender')\n\ntotals = []\n\nfor i in ax.patches:\n    totals.append(i.get_width())\n\ntotal = sum(totals)\n\nfor i in ax.patches:\n    ax.text(i.get_x()+.15, i.get_height()-3.5, \n            str(round((i.get_height()\/total), 1))+'%',\n            fontsize=12,\n            color='white',\n           weight = 'bold')<\/code><\/pre>\n<\/div>\n<p>\u305d\u3057\u3066\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30b0\u30e9\u30d5\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/p>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/gender.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/gender.png\" alt=\"\" width=\"382\" height=\"278\" class=\"aligncenter size-full wp-image-28129\" srcset=\"\/wp-content\/uploads\/2022\/03\/gender.png 382w, \/wp-content\/uploads\/2022\/03\/gender-300x218.png 300w\" sizes=\"(max-width: 382px) 100vw, 382px\" \/><\/a><\/p>\n<p>\u5225\u306e\u4f8b\u3068\u3057\u3066\u3001\u540c\u3058\u3088\u3046\u306a\u30b3\u30fc\u30c9\u3092\u4f7f\u3063\u3066\u3001\u9867\u5ba2\u3092\u30b7\u30cb\u30a2\u5c64\u5225\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">ax = (telecom_customers['SeniorCitizen'].value_counts()*100.0 \/len(telecom_customers))\n.plot.pie(autopct='%.1f%%', labels = ['No', 'Yes'],figsize =(5,5), fontsize = 12 )                                                                          \n \nax.set_ylabel('Senior Citizens',fontsize = 12)\nax.set_title('% of Senior Citizens', fontsize = 12)<\/code><\/pre>\n<\/div>\n<p>\u4ee5\u4e0b\u306e\u5186\u30b0\u30e9\u30d5\u304c\u5f97\u3089\u308c\u307e\u3059\u3002<\/p>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/seniorCit.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/03\/seniorCit.png\" alt=\"\" width=\"303\" height=\"302\" class=\"aligncenter size-full wp-image-28127\" srcset=\"\/wp-content\/uploads\/2022\/03\/seniorCit.png 303w, \/wp-content\/uploads\/2022\/03\/seniorCit-300x300.png 300w, \/wp-content\/uploads\/2022\/03\/seniorCit-150x150.png 150w, \/wp-content\/uploads\/2022\/03\/seniorCit-230x230.png 230w\" sizes=\"(max-width: 303px) 100vw, 303px\" \/><\/a><\/p>\n<p>\u3055\u3066\u3001\u30c7\u30fc\u30bf\u306e\u6e96\u5099\u304c\u3067\u304d\u305f\u306e\u3067\u3001\u3044\u3088\u3044\u3088\u9867\u5ba2\u306e\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002\u6b21\u306e\u7ae0\u3067\u306f\u3001\u4eca\u56de\u4f7f\u7528\u3057\u305f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3067\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<h2>\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb<\/h2>\n<p>\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u524d\u306b\u3001\u6211\u3005\u306f\u3059\u3067\u306b\u3059\u3079\u3066\u306e\u30ab\u30c6\u30b4\u30ea\u30fc\u5909\u6570\u3092\u30c0\u30df\u30fc\u5c5e\u6027\u306b\u5909\u63db\u3057\u305f\u3053\u3068\u3092\u601d\u3044\u8d77\u3053\u3059\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u51e6\u7406\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5168\u3066\u306e\u5c5e\u6027\u306b\u5bfe\u3057\u3066\u6a5f\u68b0\u5b66\u7fd2\u306e\u5b9f\u88c5\u304c\u5bb9\u6613\u306b\u306a\u308a\u3001\u8907\u6570\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u306a\u304f\u306a\u308a\u307e\u3059\u3002\u5b9f\u969b\u3001\u6211\u3005\u306f\u4ee5\u524d\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u4f5c\u6210\u3057\u305f\u3053\u306e\u30c0\u30df\u30fc\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u7528\u3057\u3001\u307e\u305f\u3001\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u9069\u3057\u305f\u3088\u3046\u306b\u3001\u5909\u6570\u30920\u30681\u306e\u9593\u306e\u5024\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u4e88\u5b9a\u3067\u3059\u3002\u3053\u306e2\u3064\u306e\u64cd\u4f5c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3055\u308c\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">y = dataframe_dummies['Churn'].values\nX = dataframe_dummies.drop(columns = ['Churn'])\n \nfrom sklearn.preprocessing import MinMaxScaler\nfeatures = X.columns.values\nscaler = MinMaxScaler(feature_range = (0,1))\nscaler.fit(X)\nX = pand.DataFrame(scaler.transform(X))\nX.columns = features<\/code><\/pre>\n<\/div>\n<p>\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u5b66\u7fd2\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u4e21\u65b9\u3092\u63d0\u4f9b\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u5206\u5272\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3055\u308c\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)\n<\/code><\/pre>\n<\/div>\n<p>\u4eca\u56de\u306e\u7814\u7a76\u3067\u306f\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u7528\u3044\u3066\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u5b66\u7fd2\u30c7\u30fc\u30bf\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">from sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression()\nresult = model.fit(X_train, y_train)<\/code><\/pre>\n<\/div>\n<h2>\u30e2\u30c7\u30eb\u8a55\u4fa1<\/h2>\n<p>\u3053\u306e\u6642\u70b9\u3067\u3001\u30e2\u30c7\u30eb\u3092\u5b9f\u884c\u3059\u308b\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002\u305d\u306e\u305f\u3081\u306b\u3001<code>LogisticRegression<\/code>\u30e9\u30a4\u30d6\u30e9\u30ea\u306epredict()\u95a2\u6570\u3092\u7528\u3044\u3066\u3001\u5b9f\u969b\u306e\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002\u5f97\u3089\u308c\u305f\u7cbe\u5ea6\u306f\u3001\u6b63\u3057\u304f\u5206\u985e\u3055\u308c\u305f\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e80%\u3067\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">from sklearn import metrics\nfrom sklearn.metrics import classification_report, confusion_matrix  \n \nprediction_test = model.predict(X_test)\n \n# Print results & confusion matrix\nprint (metrics.accuracy_score(y_test, prediction_test))\n \nprint(confusion_matrix(y_test,prediction_test))\n<\/code><\/pre>\n<\/div>\n<p><code>0.8075829383886256<\/code><\/p>\n<p>\u3067\u306f\u3001\u6df7\u540c\u884c\u5217\u306e\u89e3\u91c8\u306e\u4ed5\u65b9\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">[1418  162]\n[ 244  286]<\/code><\/pre>\n<\/div>\n<p>\u3053\u306e\u7d50\u679c\u304b\u3089\u30011418\u3068286\u304c\u305d\u308c\u305e\u308c\u771f\u967d\u6027\u3068\u507d\u967d\u6027\u3001\u3064\u307e\u308a\u6b63\u3057\u304f\u5206\u985e\u3055\u308c\u305f\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\uff08\u3053\u306e\u5834\u5408\u3001\u9867\u5ba2\u304c\u89e3\u7d04\u3059\u308b\u53ef\u80fd\u6027\uff09\u3067\u3042\u308b\u3053\u3068\u304c\u89b3\u5bdf\u3055\u308c\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e\u5408\u8a08\u306f1704\u3067\u3042\u308a\u3001\u51682110\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e80%\u306b\u76f8\u5f53\u3057\u307e\u3059\u3002<\/p>\n<h2>\u7d50\u8ad6<\/h2>\n<p>\u4eca\u56de\u306fPython\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u3066\u3001\u901a\u4fe1\u696d\u754c\u306e\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3057\u3066\u307f\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3059\u308b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066GridDB\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>\u3053\u306e\u8a18\u4e8b\u3092\u3055\u3089\u306b\u4e00\u6b69\u9032\u3081\u305f\u3044\u5834\u5408\u306f\u3001\u3053\u306e\u8a18\u4e8b\u3067\u63d0\u4f9b\u3055\u308c\u308b\u4f8b\u3068\u540c\u69d8\u306e\u65b9\u6cd5\u3067\u5b9f\u884c\u3067\u304d\u308b\u4ed6\u306e\u5206\u985e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u8a66\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u8a73\u3057\u304f\u306f\u3001<a href=\"https:\/\/www.kaggle.com\/bandiatindra\/telecom-churn-prediction\/notebook\">\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u30ce\u30fc\u30c8\u30d6\u30c3\u30af<\/a>\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2>\u53c2\u8003\u6587\u732e<\/h2>\n<ul>\n<li>https:\/\/www.heavy.ai\/blog\/strategies-for-reducing-churn-rate-in-the-telecom-industry<\/li>\n<li><a href=\"https:\/\/www.kaggle.com\/bandiatindra\/telecom-churn-prediction\/notebook\">https:\/\/www.kaggle.com\/bandiatindra\/telecom-churn-prediction\/notebook<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u9867\u5ba2\u89e3\u7d04\u7387\u3068\u306f\u3001\u7279\u5b9a\u306e\u4f01\u696d\u3068\u306e\u53d6\u5f15\u3092\u505c\u6b62\u3059\u308b\u9867\u5ba2\u306e\u6570\u3092\u6c7a\u5b9a\u3059\u308b\u91cd\u8981\u306a\u30d3\u30b8\u30cd\u30b9\u6982\u5ff5\u3067\u3059\u3002\u89e3\u7d04\u7387\u306f\u3001\u4f01\u696d\u304c\u4e00\u5b9a\u306e\u671f\u9593\u306b\u9867\u5ba2\u3092\u5931\u3046\u5272\u5408\u3068\u5b9a\u7fa9\u3055\u308c\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u89e3\u7d04\u7387\u304c\u5e7415%\u3067\u3042\u308c\u3070\u3001\u305d\u306e\u4f01\u696d\u306f\u6bce\u5e74\u7dcf\u9867\u5ba2\u6570\u306e15%\u3092\u5931\u3063\u3066\u3044\u308b [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49379,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50797","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u6a5f\u68b0\u5b66\u7fd2\u3001Python\u3001GridDB\u3092\u7528\u3044\u3066\u901a\u4fe1\u4e8b\u696d\u8005\u306e\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3059\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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